Overview

Dataset statistics

Number of variables15
Number of observations931
Missing cells1037
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory109.2 KiB
Average record size in memory120.1 B

Variable types

Categorical1
Numeric14

Alerts

region has a high cardinality: 85 distinct valuesHigh cardinality
region_code is highly overall correlated with regionHigh correlation
population is highly overall correlated with unemployed_pop and 8 other fieldsHigh correlation
unemployed_pop is highly overall correlated with population and 8 other fieldsHigh correlation
poor_pop is highly overall correlated with population and 8 other fieldsHigh correlation
disabled_people is highly overall correlated with population and 8 other fieldsHigh correlation
divorces is highly overall correlated with population and 8 other fieldsHigh correlation
orphans is highly overall correlated with population and 8 other fieldsHigh correlation
alcohol_sold is highly overall correlated with population and 7 other fieldsHigh correlation
abortions is highly overall correlated with population and 7 other fieldsHigh correlation
crimes is highly overall correlated with population and 8 other fieldsHigh correlation
region is highly overall correlated with region_code and 7 other fieldsHigh correlation
poor_pop has 85 (9.1%) missing valuesMissing
median_salary has 340 (36.5%) missing valuesMissing
divorces has 88 (9.5%) missing valuesMissing
orphans has 425 (45.6%) missing valuesMissing
abortions has 85 (9.1%) missing valuesMissing
region is uniformly distributedUniform
year is uniformly distributedUniform

Reproduction

Analysis started2023-05-19 11:57:47.300372
Analysis finished2023-05-19 11:57:59.081404
Duration11.78 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

region
Categorical

HIGH CARDINALITY  HIGH CORRELATION  UNIFORM 

Distinct85
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
Алтайский край
 
11
Республика Коми
 
11
Ростовская область
 
11
Республика Хакасия
 
11
Республика Тыва
 
11
Other values (80)
876 

Length

Max length40
Median length29
Mean length19.3029
Min length8

Characters and Unicode

Total characters17971
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowАлтайский край
2nd rowАлтайский край
3rd rowАлтайский край
4th rowАлтайский край
5th rowАлтайский край

Common Values

ValueCountFrequency (%)
Алтайский край 11
 
1.2%
Республика Коми 11
 
1.2%
Ростовская область 11
 
1.2%
Республика Хакасия 11
 
1.2%
Республика Тыва 11
 
1.2%
Республика Татарстан 11
 
1.2%
Республика Северная Осетия – Алания 11
 
1.2%
Республика Саха (Якутия) 11
 
1.2%
Республика Мордовия 11
 
1.2%
Республика Марий Эл 11
 
1.2%
Other values (75) 821
88.2%

Length

2023-05-19T14:57:59.120335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
область 517
25.9%
республика 240
 
12.0%
край 99
 
5.0%
автономный 44
 
2.2%
округ 44
 
2.2%
33
 
1.7%
алтайский 11
 
0.6%
г.санкт-петербург 11
 
0.6%
белгородская 11
 
0.6%
брянская 11
 
0.6%
Other values (89) 973
48.8%

Most occurring characters

ValueCountFrequency (%)
а 2262
 
12.6%
с 1657
 
9.2%
к 1241
 
6.9%
о 1239
 
6.9%
1063
 
5.9%
л 1041
 
5.8%
б 856
 
4.8%
т 834
 
4.6%
я 770
 
4.3%
р 735
 
4.1%
Other values (46) 6273
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15475
86.1%
Uppercase Letter 1292
 
7.2%
Space Separator 1063
 
5.9%
Dash Punctuation 88
 
0.5%
Other Punctuation 31
 
0.2%
Open Punctuation 11
 
0.1%
Close Punctuation 11
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
а 2262
14.6%
с 1657
 
10.7%
к 1241
 
8.0%
о 1239
 
8.0%
л 1041
 
6.7%
б 856
 
5.5%
т 834
 
5.4%
я 770
 
5.0%
р 735
 
4.7%
и 669
 
4.3%
Other values (18) 4171
27.0%
Uppercase Letter
ValueCountFrequency (%)
Р 262
20.3%
К 185
14.3%
С 108
 
8.4%
Т 77
 
6.0%
М 77
 
6.0%
А 77
 
6.0%
Ч 55
 
4.3%
Н 55
 
4.3%
П 55
 
4.3%
Б 55
 
4.3%
Other values (12) 286
22.1%
Dash Punctuation
ValueCountFrequency (%)
- 55
62.5%
33
37.5%
Space Separator
ValueCountFrequency (%)
1063
100.0%
Other Punctuation
ValueCountFrequency (%)
. 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 16767
93.3%
Common 1204
 
6.7%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
а 2262
13.5%
с 1657
 
9.9%
к 1241
 
7.4%
о 1239
 
7.4%
л 1041
 
6.2%
б 856
 
5.1%
т 834
 
5.0%
я 770
 
4.6%
р 735
 
4.4%
и 669
 
4.0%
Other values (40) 5463
32.6%
Common
ValueCountFrequency (%)
1063
88.3%
- 55
 
4.6%
33
 
2.7%
. 31
 
2.6%
( 11
 
0.9%
) 11
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 16767
93.3%
ASCII 1171
 
6.5%
Punctuation 33
 
0.2%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
а 2262
13.5%
с 1657
 
9.9%
к 1241
 
7.4%
о 1239
 
7.4%
л 1041
 
6.2%
б 856
 
5.1%
т 834
 
5.0%
я 770
 
4.6%
р 735
 
4.4%
и 669
 
4.0%
Other values (40) 5463
32.6%
ASCII
ValueCountFrequency (%)
1063
90.8%
- 55
 
4.7%
. 31
 
2.6%
( 11
 
0.9%
) 11
 
0.9%
Punctuation
ValueCountFrequency (%)
33
100.0%

region_code
Real number (ℝ)

Distinct85
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.553308
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:57:59.185723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q122
median42
Q363
95-th percentile86
Maximum92
Range91
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.728182
Coefficient of variation (CV)0.58111068
Kurtosis-1.0485902
Mean42.553308
Median Absolute Deviation (MAD)21
Skewness0.11281995
Sum39617.13
Variance611.48298
MonotonicityNot monotonic
2023-05-19T14:57:59.247982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 11
 
1.2%
11 11
 
1.2%
61 11
 
1.2%
19 11
 
1.2%
17 11
 
1.2%
16 11
 
1.2%
15 11
 
1.2%
14 11
 
1.2%
13 11
 
1.2%
12 11
 
1.2%
Other values (75) 821
88.2%
ValueCountFrequency (%)
1 11
1.2%
2 11
1.2%
3 11
1.2%
4 11
1.2%
5 11
1.2%
6 11
1.2%
7 11
1.2%
8 11
1.2%
9 11
1.2%
10 11
1.2%
ValueCountFrequency (%)
92 9
1.0%
91 9
1.0%
89 11
1.2%
87 11
1.2%
86 11
1.2%
79 11
1.2%
78 11
1.2%
77 11
1.2%
76 11
1.2%
75 11
1.2%

year
Real number (ℝ)

Distinct11
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.0193
Minimum2012
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:57:59.300157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2017
Q32020
95-th percentile2022
Maximum2022
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1567732
Coefficient of variation (CV)0.0015650684
Kurtosis-1.2152477
Mean2017.0193
Median Absolute Deviation (MAD)3
Skewness-0.0054653617
Sum1877845
Variance9.9652172
MonotonicityNot monotonic
2023-05-19T14:57:59.345886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2014 85
9.1%
2015 85
9.1%
2016 85
9.1%
2017 85
9.1%
2018 85
9.1%
2019 85
9.1%
2020 85
9.1%
2021 85
9.1%
2022 85
9.1%
2012 83
8.9%
ValueCountFrequency (%)
2012 83
8.9%
2013 83
8.9%
2014 85
9.1%
2015 85
9.1%
2016 85
9.1%
2017 85
9.1%
2018 85
9.1%
2019 85
9.1%
2020 85
9.1%
2021 85
9.1%
ValueCountFrequency (%)
2022 85
9.1%
2021 85
9.1%
2020 85
9.1%
2019 85
9.1%
2018 85
9.1%
2017 85
9.1%
2016 85
9.1%
2015 85
9.1%
2014 85
9.1%
2013 83
8.9%

population
Real number (ℝ)

Distinct918
Distinct (%)98.8%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1750.5574
Minimum42.4
Maximum12678.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:57:59.401232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum42.4
5-th percentile212.44
Q1733.5
median1187.7
Q32376.7
95-th percentile4326.5424
Maximum12678.1
Range12635.7
Interquartile range (IQR)1643.2

Descriptive statistics

Standard deviation1777.6746
Coefficient of variation (CV)1.0154906
Kurtosis14.050358
Mean1750.5574
Median Absolute Deviation (MAD)591.2
Skewness3.1429899
Sum1626267.9
Variance3160126.9
MonotonicityNot monotonic
2023-05-19T14:57:59.467906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
534.1 2
 
0.2%
1144.1 2
 
0.2%
1342.1 2
 
0.2%
954.8 2
 
0.2%
314.7 2
 
0.2%
1895.9 2
 
0.2%
818.6 2
 
0.2%
311.7 2
 
0.2%
50.5 2
 
0.2%
449.2 2
 
0.2%
Other values (908) 909
97.6%
ValueCountFrequency (%)
42.4 1
0.1%
42.8 1
0.1%
43 1
0.1%
43.4 1
0.1%
43.8 1
0.1%
43.9 2
0.2%
43.997 1
0.1%
44.1 1
0.1%
44.4 1
0.1%
44.5 1
0.1%
ValueCountFrequency (%)
12678.1 1
0.1%
12655.1 1
0.1%
12635.5 1
0.1%
12615.3 1
0.1%
12506.468 1
0.1%
12380.7 1
0.1%
12330.1 1
0.1%
12197.6 1
0.1%
12108.3 1
0.1%
11979.5 1
0.1%

unemployed_pop
Real number (ℝ)

Distinct925
Distinct (%)99.6%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean46.408461
Minimum0.579
Maximum206.399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:57:59.534418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.579
5-th percentile6.8732
Q122.315
median34.353
Q365.049
95-th percentile125.5322
Maximum206.399
Range205.82
Interquartile range (IQR)42.734

Descriptive statistics

Standard deviation36.372855
Coefficient of variation (CV)0.78375482
Kurtosis2.098425
Mean46.408461
Median Absolute Deviation (MAD)16.199
Skewness1.466222
Sum43113.46
Variance1322.9846
MonotonicityNot monotonic
2023-05-19T14:57:59.594576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.783 2
 
0.2%
51.94 2
 
0.2%
44.67 2
 
0.2%
23.825 2
 
0.2%
37.426 1
 
0.1%
18.983 1
 
0.1%
18.91 1
 
0.1%
18.759 1
 
0.1%
18.46 1
 
0.1%
17.725 1
 
0.1%
Other values (915) 915
98.3%
(Missing) 2
 
0.2%
ValueCountFrequency (%)
0.579 1
0.1%
0.809 1
0.1%
0.916 1
0.1%
0.932 1
0.1%
1.077 1
0.1%
1.08 1
0.1%
1.142 1
0.1%
1.171 1
0.1%
1.199 1
0.1%
1.296 1
0.1%
ValueCountFrequency (%)
206.399 1
0.1%
201.515 1
0.1%
192.912 1
0.1%
191.613 1
0.1%
179.307 1
0.1%
177.891 1
0.1%
174.724 1
0.1%
173.209 1
0.1%
163.379 1
0.1%
162.471 1
0.1%

poor_pop
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct742
Distinct (%)87.7%
Missing85
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean212.11927
Minimum3.3
Maximum1085.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:57:59.656803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile36.425
Q1103.8
median159.7
Q3296.9
95-th percentile500.65
Maximum1085.4
Range1082.1
Interquartile range (IQR)193.1

Descriptive statistics

Standard deviation159.98306
Coefficient of variation (CV)0.75421278
Kurtosis3.8066684
Mean212.11927
Median Absolute Deviation (MAD)67.75
Skewness1.5821704
Sum179452.9
Variance25594.58
MonotonicityNot monotonic
2023-05-19T14:57:59.720122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1 4
 
0.4%
4.3 4
 
0.4%
148.9 3
 
0.3%
155.3 3
 
0.3%
87.7 3
 
0.3%
148.6 3
 
0.3%
124.3 3
 
0.3%
97.3 3
 
0.3%
396.5 3
 
0.3%
102.7 3
 
0.3%
Other values (732) 814
87.4%
(Missing) 85
 
9.1%
ValueCountFrequency (%)
3.3 1
 
0.1%
3.4 1
 
0.1%
3.6 1
 
0.1%
3.8 2
0.2%
3.9 1
 
0.1%
4 2
0.2%
4.1 4
0.4%
4.2 2
0.2%
4.3 4
0.4%
4.4 1
 
0.1%
ValueCountFrequency (%)
1085.4 1
0.1%
1070.5 1
0.1%
1047.8 1
0.1%
1046 1
0.1%
922.6 1
0.1%
831.7 1
0.1%
803.6 1
0.1%
758.2 1
0.1%
701.5 1
0.1%
639.7 1
0.1%

disabled_people
Real number (ℝ)

Distinct457
Distinct (%)49.4%
Missing5
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean151560.19
Minimum1912
Maximum1638000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:57:59.783453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1912
5-th percentile16000
Q163000
median105074
Q3195000
95-th percentile375500
Maximum1638000
Range1636088
Interquartile range (IQR)132000

Descriptive statistics

Standard deviation179231.63
Coefficient of variation (CV)1.1825772
Kurtosis29.83177
Mean151560.19
Median Absolute Deviation (MAD)51074
Skewness4.6170089
Sum1.4034474 × 108
Variance3.2123977 × 1010
MonotonicityNot monotonic
2023-05-19T14:57:59.848684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23000 15
 
1.6%
65000 12
 
1.3%
82000 12
 
1.3%
17000 11
 
1.2%
148000 10
 
1.1%
58000 9
 
1.0%
3000 9
 
1.0%
74000 9
 
1.0%
2000 9
 
1.0%
66000 8
 
0.9%
Other values (447) 822
88.3%
ValueCountFrequency (%)
1912 1
 
0.1%
2000 9
1.0%
2999 1
 
0.1%
3000 9
1.0%
3110 1
 
0.1%
4106 1
 
0.1%
5000 3
 
0.3%
5426 1
 
0.1%
6000 5
0.5%
7000 1
 
0.1%
ValueCountFrequency (%)
1638000 1
0.1%
1634000 1
0.1%
1615000 1
0.1%
1592000 1
0.1%
1539000 1
0.1%
1537000 1
0.1%
1088000 1
0.1%
1070000 1
0.1%
1051000 1
0.1%
1018773 1
0.1%

avr_salary
Real number (ℝ)

Distinct673
Distinct (%)72.6%
Missing4
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean102.35641
Minimum85.128237
Maximum122.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:57:59.916350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum85.128237
5-th percentile90.943659
Q199.755286
median102.9
Q3105.3
95-th percentile110.41714
Maximum122.4
Range37.271763
Interquartile range (IQR)5.5447145

Descriptive statistics

Standard deviation5.3239725
Coefficient of variation (CV)0.05201406
Kurtosis0.76945201
Mean102.35641
Median Absolute Deviation (MAD)2.8
Skewness-0.43978251
Sum94884.394
Variance28.344683
MonotonicityNot monotonic
2023-05-19T14:57:59.977971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.8 8
 
0.9%
104.2 7
 
0.8%
105.3 7
 
0.8%
102.3 6
 
0.6%
99 6
 
0.6%
107.4 6
 
0.6%
103.2 6
 
0.6%
105.1 6
 
0.6%
101.9 5
 
0.5%
105.2 5
 
0.5%
Other values (663) 865
92.9%
ValueCountFrequency (%)
85.12823731 1
0.1%
87.78617673 1
0.1%
87.9 1
0.1%
88.0064365 1
0.1%
88.2 1
0.1%
88.2684468 1
0.1%
88.3999877 1
0.1%
88.6 1
0.1%
88.80097654 1
0.1%
88.9 1
0.1%
ValueCountFrequency (%)
122.4 1
0.1%
119.8 1
0.1%
117.8 1
0.1%
117.1 1
0.1%
116.4 1
0.1%
115 1
0.1%
114.8969543 1
0.1%
114.3 1
0.1%
114 1
0.1%
113.6915836 1
0.1%

median_salary
Real number (ℝ)

Distinct588
Distinct (%)99.5%
Missing340
Missing (%)36.5%
Infinite0
Infinite (%)0.0%
Mean21941.027
Minimum8904.3
Maximum62682.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:58:00.043054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8904.3
5-th percentile13304.9
Q116927.35
median19639.8
Q323385.4
95-th percentile41015.35
Maximum62682.5
Range53778.2
Interquartile range (IQR)6458.05

Descriptive statistics

Standard deviation8588.7487
Coefficient of variation (CV)0.39144699
Kurtosis5.095005
Mean21941.027
Median Absolute Deviation (MAD)3087
Skewness2.0950451
Sum12967147
Variance73766605
MonotonicityNot monotonic
2023-05-19T14:58:00.108568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21897.5 2
 
0.2%
17945.4 2
 
0.2%
19419.9 2
 
0.2%
34313.8 1
 
0.1%
32227.8 1
 
0.1%
30597.4 1
 
0.1%
14147 1
 
0.1%
29849.2 1
 
0.1%
28873 1
 
0.1%
11457.5 1
 
0.1%
Other values (578) 578
62.1%
(Missing) 340
36.5%
ValueCountFrequency (%)
8904.3 1
0.1%
9765.9 1
0.1%
10578.3 1
0.1%
10950.8 1
0.1%
11078.2 1
0.1%
11209.7 1
0.1%
11345 1
0.1%
11361.1 1
0.1%
11402.6 1
0.1%
11447 1
0.1%
ValueCountFrequency (%)
62682.5 1
0.1%
59578.7 1
0.1%
59526.4 1
0.1%
58224.4 1
0.1%
57348.2 1
0.1%
57058 1
0.1%
55004.2 1
0.1%
54112.5 1
0.1%
52650 1
0.1%
52275.9 1
0.1%

new_houses
Real number (ℝ)

Distinct583
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.06817
Minimum8
Maximum1970
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:58:00.173405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile179
Q1344
median473.33124
Q3611.5
95-th percentile917.5
Maximum1970
Range1962
Interquartile range (IQR)267.5

Descriptive statistics

Standard deviation244.09886
Coefficient of variation (CV)0.48618668
Kurtosis4.0937071
Mean502.06817
Median Absolute Deviation (MAD)133.33124
Skewness1.3605493
Sum467425.47
Variance59584.251
MonotonicityNot monotonic
2023-05-19T14:58:00.236440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
380 6
 
0.6%
275 6
 
0.6%
490 5
 
0.5%
323 5
 
0.5%
462 5
 
0.5%
476 5
 
0.5%
435 5
 
0.5%
475 5
 
0.5%
419 5
 
0.5%
437 5
 
0.5%
Other values (573) 879
94.4%
ValueCountFrequency (%)
8 1
 
0.1%
24 1
 
0.1%
26 1
 
0.1%
30 1
 
0.1%
31 1
 
0.1%
32 1
 
0.1%
33 1
 
0.1%
34 1
 
0.1%
36 3
0.3%
44 3
0.3%
ValueCountFrequency (%)
1970 1
0.1%
1780.326767 1
0.1%
1645 1
0.1%
1574 1
0.1%
1456 1
0.1%
1442 1
0.1%
1415 1
0.1%
1385 1
0.1%
1340 1
0.1%
1323 2
0.2%

divorces
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct814
Distinct (%)96.6%
Missing88
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean7516.0543
Minimum6.8
Maximum47980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:58:00.302100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6.8
5-th percentile744.5
Q12866
median5005
Q310137.5
95-th percentile21444.6
Maximum47980
Range47973.2
Interquartile range (IQR)7271.5

Descriptive statistics

Standard deviation7465.7824
Coefficient of variation (CV)0.99331139
Kurtosis7.5348566
Mean7516.0543
Median Absolute Deviation (MAD)2540
Skewness2.4195852
Sum6336033.8
Variance55737907
MonotonicityNot monotonic
2023-05-19T14:58:00.478754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4871 3
 
0.3%
227 3
 
0.3%
1042 3
 
0.3%
176 2
 
0.2%
3881 2
 
0.2%
5073 2
 
0.2%
4756 2
 
0.2%
4083 2
 
0.2%
4657 2
 
0.2%
4571 2
 
0.2%
Other values (804) 820
88.1%
(Missing) 88
 
9.5%
ValueCountFrequency (%)
6.8 1
 
0.1%
7 1
 
0.1%
136 1
 
0.1%
158 1
 
0.1%
163 1
 
0.1%
173 1
 
0.1%
176 2
0.2%
185 1
 
0.1%
197 1
 
0.1%
227 3
0.3%
ValueCountFrequency (%)
47980 1
0.1%
46583 1
0.1%
45682 1
0.1%
45378 1
0.1%
45009 1
0.1%
43942 1
0.1%
43560 1
0.1%
43288 1
0.1%
42385 1
0.1%
39934 1
0.1%

orphans
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct405
Distinct (%)80.0%
Missing425
Missing (%)45.6%
Infinite0
Infinite (%)0.0%
Mean516.11265
Minimum0
Maximum2410
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:58:00.541509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.25
Q1187.25
median414.5
Q3677.5
95-th percentile1402.75
Maximum2410
Range2410
Interquartile range (IQR)490.25

Descriptive statistics

Standard deviation424.41791
Coefficient of variation (CV)0.82233581
Kurtosis1.1356402
Mean516.11265
Median Absolute Deviation (MAD)232.5
Skewness1.2261815
Sum261153
Variance180130.56
MonotonicityNot monotonic
2023-05-19T14:58:00.605169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
536 5
 
0.5%
93 4
 
0.4%
305 4
 
0.4%
177 4
 
0.4%
625 3
 
0.3%
152 3
 
0.3%
265 3
 
0.3%
263 3
 
0.3%
225 3
 
0.3%
125 3
 
0.3%
Other values (395) 471
50.6%
(Missing) 425
45.6%
ValueCountFrequency (%)
0 1
0.1%
5 2
0.2%
8 1
0.1%
14 1
0.1%
19 1
0.1%
29 1
0.1%
33 2
0.2%
34 1
0.1%
35 1
0.1%
36 2
0.2%
ValueCountFrequency (%)
2410 1
0.1%
1904 1
0.1%
1841 1
0.1%
1771 1
0.1%
1769 1
0.1%
1728 1
0.1%
1682 1
0.1%
1643 1
0.1%
1642 1
0.1%
1625 1
0.1%

alcohol_sold
Real number (ℝ)

Distinct925
Distinct (%)99.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2943.6032
Minimum0
Maximum53424
Zeros5
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:58:00.666623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile113.39375
Q11047.8
median1786.2015
Q33096.1213
95-th percentile7412.8199
Maximum53424
Range53424
Interquartile range (IQR)2048.3212

Descriptive statistics

Standard deviation4914.5164
Coefficient of variation (CV)1.6695581
Kurtosis44.315883
Mean2943.6032
Median Absolute Deviation (MAD)898.091
Skewness5.9267776
Sum2737550.9
Variance24152472
MonotonicityNot monotonic
2023-05-19T14:58:00.731344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.5%
432.8 2
 
0.2%
4200 1
 
0.1%
1524.9 1
 
0.1%
1331.2 1
 
0.1%
1314.4 1
 
0.1%
943.5 1
 
0.1%
913.314 1
 
0.1%
965.391 1
 
0.1%
945.249 1
 
0.1%
Other values (915) 915
98.3%
ValueCountFrequency (%)
0 5
0.5%
0.5 1
 
0.1%
0.9 1
 
0.1%
1.6 1
 
0.1%
2.2 1
 
0.1%
4.6 1
 
0.1%
11.829 1
 
0.1%
11.999 1
 
0.1%
16.145 1
 
0.1%
17.434 1
 
0.1%
ValueCountFrequency (%)
53424 1
0.1%
48805 1
0.1%
48345 1
0.1%
44672 1
0.1%
42350 1
0.1%
33887 1
0.1%
33555.7 1
0.1%
32648.3 1
0.1%
31636.3 1
0.1%
25866.8 1
0.1%

abortions
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct824
Distinct (%)97.4%
Missing85
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean9436.9622
Minimum234
Maximum48480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:58:00.793975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum234
5-th percentile1136.75
Q13789.5
median6631
Q312300.75
95-th percentile27118
Maximum48480
Range48246
Interquartile range (IQR)8511.25

Descriptive statistics

Standard deviation8300.8066
Coefficient of variation (CV)0.87960579
Kurtosis2.4265156
Mean9436.9622
Median Absolute Deviation (MAD)3448.5
Skewness1.580042
Sum7983670
Variance68903390
MonotonicityNot monotonic
2023-05-19T14:58:00.857413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3722 3
 
0.3%
967 2
 
0.2%
8253 2
 
0.2%
7264 2
 
0.2%
9967 2
 
0.2%
5169 2
 
0.2%
4545 2
 
0.2%
16024 2
 
0.2%
6638 2
 
0.2%
2514 2
 
0.2%
Other values (814) 825
88.6%
(Missing) 85
 
9.1%
ValueCountFrequency (%)
234 1
0.1%
287 1
0.1%
288 1
0.1%
293 1
0.1%
294 1
0.1%
315 1
0.1%
317 1
0.1%
318 1
0.1%
362 1
0.1%
375 1
0.1%
ValueCountFrequency (%)
48480 1
0.1%
46174 1
0.1%
42703 1
0.1%
42489 1
0.1%
41320 1
0.1%
38283 1
0.1%
38270 1
0.1%
38040 1
0.1%
37454 1
0.1%
36378 1
0.1%

crimes
Real number (ℝ)

Distinct911
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22966.398
Minimum511
Maximum109349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2023-05-19T14:58:00.920505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum511
5-th percentile2968.5
Q19677
median16366
Q330404.5
95-th percentile62484
Maximum109349
Range108838
Interquartile range (IQR)20727.5

Descriptive statistics

Standard deviation18741.224
Coefficient of variation (CV)0.81602799
Kurtosis1.3274438
Mean22966.398
Median Absolute Deviation (MAD)8397
Skewness1.306281
Sum21381717
Variance3.5123348 × 108
MonotonicityNot monotonic
2023-05-19T14:58:00.984067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29552 2
 
0.2%
50751 2
 
0.2%
12334 2
 
0.2%
13274 2
 
0.2%
2951 2
 
0.2%
27682 2
 
0.2%
30437 2
 
0.2%
33747 2
 
0.2%
39298 2
 
0.2%
34370 2
 
0.2%
Other values (901) 911
97.9%
ValueCountFrequency (%)
511 1
0.1%
627 1
0.1%
645 1
0.1%
655 1
0.1%
686 1
0.1%
712 1
0.1%
732 1
0.1%
748 1
0.1%
754 1
0.1%
763 1
0.1%
ValueCountFrequency (%)
109349 1
0.1%
107178 1
0.1%
91061 1
0.1%
89183 1
0.1%
88297 1
0.1%
84307 1
0.1%
80299 1
0.1%
78183 1
0.1%
77125 1
0.1%
77049 1
0.1%

Interactions

2023-05-19T14:57:57.876659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:47.557873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.316148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.099833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.910091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.820087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.588903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.416302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.219738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.961975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.886690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.640773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.357627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.124371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.928873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:47.610656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.370562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.155395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.961511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.873385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.646642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.472039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.269554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.017040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.938188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.688752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.410668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.177261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.984079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:47.664872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.426003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.215787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.016469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.926039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.704915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.531950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.324854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.076946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.989495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.738972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.466953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.228820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:58.040574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:47.720044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.482343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.273035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.070936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.982982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.766335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.590097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.377552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.135444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.045179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.790411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.522628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.284075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:58.096611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:47.771540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.537274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.327434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.123661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.035750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.824652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.645304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.433765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.193597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.096313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.845268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.575875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.335659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:58.151362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:47.824459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.591688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.382143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.175547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.089876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.882034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.699800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.485528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.249869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.149195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.895494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.628732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.387843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:58.214286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:47.885772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.653282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.445134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.235922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.150057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.947192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.764702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.544092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.315845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.208682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.952392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.691413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.449669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:58.273712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:47.943216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.713706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.504602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.293410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.206728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.009870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.824103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.598157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.377204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.266478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.005448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.749895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.508311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:58.437975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:47.993934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.767466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.559858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.346908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.258289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.066921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.878803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.649792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.542884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.319214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.056826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.801293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.557838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:58.498747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.053830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.828944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.623657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.405596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.319760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.130651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.941705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.706383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.607188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.377774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.111252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.860911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.615811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:58.554827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.106120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.880653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.682003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.459169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.374833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.188496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.997709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.757765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.662040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.431842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.159183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.914839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.668169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:58.605229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.155748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.930597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.734981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.510254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.430910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.241748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.049410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.809678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.714872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.482004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.207537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.963796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.718640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:58.659096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.208395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.987777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.794457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.564580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.484574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.300523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.106903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.860961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.772450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.534792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.257739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.018252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.771095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:58.714082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:48.261898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.043236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:49.851241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:50.765772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:51.535051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:52.356845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.163087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:53.909527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:54.829135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:55.588918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:56.306969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.070703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-19T14:57:57.823844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-19T14:58:01.044313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
region_codeyearpopulationunemployed_poppoor_popdisabled_peopleavr_salarymedian_salarynew_housesdivorcesorphansalcohol_soldabortionscrimesregion
region_code1.0000.0100.187-0.0240.0390.130-0.0350.3130.0740.2720.1310.2600.1830.1711.000
year0.0101.000-0.016-0.117-0.064-0.082-0.1740.3720.135-0.061-0.110-0.088-0.289-0.0550.000
population0.187-0.0161.0000.8520.9090.9390.0080.1400.2850.9390.7670.8580.8660.8870.856
unemployed_pop-0.024-0.1170.8521.0000.9100.8080.001-0.0170.1170.7630.7170.6660.7670.7790.574
poor_pop0.039-0.0640.9090.9101.0000.8490.004-0.0120.1350.8330.7640.7370.8500.8360.639
disabled_people0.130-0.0820.9390.8080.8491.0000.0090.0300.2990.8670.6850.7690.7960.8010.651
avr_salary-0.035-0.1740.0080.0010.0040.0091.000-0.008-0.224-0.0190.0630.044-0.003-0.0120.000
median_salary0.3130.3720.140-0.017-0.0120.030-0.0081.0000.0490.2020.1890.2630.1120.1660.466
new_houses0.0740.1350.2850.1170.1350.299-0.2240.0491.0000.263-0.0190.2210.1560.1460.471
divorces0.272-0.0610.9390.7630.8330.867-0.0190.2020.2631.0000.8150.9060.8920.9200.728
orphans0.131-0.1100.7670.7170.7640.6850.0630.189-0.0190.8151.0000.8520.8860.9070.535
alcohol_sold0.260-0.0880.8580.6660.7370.7690.0440.2630.2210.9060.8521.0000.8720.8960.436
abortions0.183-0.2890.8660.7670.8500.796-0.0030.1120.1560.8920.8860.8721.0000.9030.436
crimes0.171-0.0550.8870.7790.8360.801-0.0120.1660.1460.9200.9070.8960.9031.0000.640
region1.0000.0000.8560.5740.6390.6510.0000.4660.4710.7280.5350.4360.4360.6401.000

Missing values

2023-05-19T14:57:58.801307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-19T14:57:58.914535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-19T14:57:59.012569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

regionregion_codeyearpopulationunemployed_poppoor_popdisabled_peopleavr_salarymedian_salarynew_housesdivorcesorphansalcohol_soldabortionscrimes
0Алтайский край22.020122407.2074.385NaN218000.0110.500000NaN269.000012288.01123.04200.00016024.042102
1Алтайский край22.020132398.7095.790422.2211000.0104.90000012545.4278.000013259.01059.03545.60016183.045024
2Алтайский край22.020142390.6083.390409.6207000.099.92865414456.2316.000013243.0932.02567.60014821.044096
3Алтайский край22.020152384.8094.779429.5203000.089.95350116554.6374.000010662.01024.02596.50013444.048582
4Алтайский край22.020162376.7099.645423.0198000.098.40000016923.2318.000010369.0975.02369.60013624.044576
5Алтайский край22.020172365.7078.187414.1191000.0103.55143817427.3267.000010745.0955.01907.63212309.040055
6Алтайский край22.020182350.0870.300408.6189000.0109.30005217948.5336.000010040.0NaN2231.25910596.038413
7Алтайский край22.020192332.8066.132409.4186000.0104.87291818899.2326.000010201.0NaN2254.57710289.037058
8Алтайский край22.020202317.1064.639404.6185000.0103.824609NaN358.000010031.0NaN2164.4928488.039029
9Алтайский край22.020212296.4060.600379.0174000.0104.400000NaN433.200610435.0NaN2181.0827563.039070
regionregion_codeyearpopulationunemployed_poppoor_popdisabled_peopleavr_salarymedian_salarynew_housesdivorcesorphansalcohol_soldabortionscrimes
921Ярославская область76.020131271.70030.179137.8140000.0104.70000016237.0383.0000006450.0461.02708.70010033.015390
922Ярославская область76.020141271.80026.412131.6137000.0101.27146618336.4546.0000006382.0375.02806.9008480.015512
923Ярославская область76.020151271.60036.753137.3133000.089.80206721218.5563.0000005445.0432.02881.9007426.021759
924Ярославская область76.020161271.90045.155140.1128000.098.69156321662.4627.0000005195.0425.02644.4007635.018401
925Ярославская область76.020171270.70044.096135.5116000.0104.04791821891.5594.0000005187.0434.02457.9326545.017873
926Ярославская область76.020181265.68436.428128.9112000.0106.00000021865.9608.0000005080.0NaN2649.3365284.016710
927Ярославская область76.020191259.60035.037129.9108000.0101.59061423183.9619.0000005400.0NaN2615.9994608.017709
928Ярославская область76.020201253.40047.400124.0104000.0100.893629NaN598.0000004669.0NaN2605.9883919.016088
929Ярославская область76.020211241.40038.999110.495358.0102.000000NaN615.7664885584.0NaN2528.6443685.016066
930Ярославская область76.020221227.40031.935108.0100553.097.700000NaN664.000000NaNNaN2596.036NaN18368